Efficient forecasting model technique for river stream flow in tropical environment
Monthly stream flow forecasting can provide crucial information on hydrological applications including water resource management and flood mitigation systems. In this statistical study, time series and artificial intelligence methods were evaluated according to implementation of each time-series tec...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Published: |
Taylor & Francis
2019
|
| Subjects: |
| _version_ | 1825722208806240256 |
|---|---|
| author | Khairuddin, Nuruljannah Aris, Ahmad Zaharin El-Shafie, Ahmed Sheikhy Narany, Tahoora Ishak, Mohd Yusoff Isa, Noorain Mohd |
| author_facet | Khairuddin, Nuruljannah Aris, Ahmad Zaharin El-Shafie, Ahmed Sheikhy Narany, Tahoora Ishak, Mohd Yusoff Isa, Noorain Mohd |
| author_sort | Khairuddin, Nuruljannah |
| collection | UM |
| description | Monthly stream flow forecasting can provide crucial information on hydrological applications including water resource management and flood mitigation systems. In this statistical study, time series and artificial intelligence methods were evaluated according to implementation of each time-series technique to find an effective tool for stream flow prediction in flood forecasting. This paper explores the application of water level, rainfall data and input time series into three different models; linear regression (LR), auto-regressive integrated moving average (ARIMA) and artificial neural networks (ANN). The performances of the models were compared based on the maximum coefficient of determination (R2) and minimum root means square error (RMSE). Based on the results the ANN model presents the most accurate measurement, with the R2 value of 0.868 and 18% RMSE. The present study suggests that ANN is the best model due to its ability to recognise times series patterns and to understand non-linear relationships. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group. |
| first_indexed | 2024-03-06T06:02:09Z |
| format | Article |
| id | um.eprints-24221 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T06:02:09Z |
| publishDate | 2019 |
| publisher | Taylor & Francis |
| record_format | dspace |
| spelling | um.eprints-242212020-04-23T11:29:55Z http://eprints.um.edu.my/24221/ Efficient forecasting model technique for river stream flow in tropical environment Khairuddin, Nuruljannah Aris, Ahmad Zaharin El-Shafie, Ahmed Sheikhy Narany, Tahoora Ishak, Mohd Yusoff Isa, Noorain Mohd TA Engineering (General). Civil engineering (General) Monthly stream flow forecasting can provide crucial information on hydrological applications including water resource management and flood mitigation systems. In this statistical study, time series and artificial intelligence methods were evaluated according to implementation of each time-series technique to find an effective tool for stream flow prediction in flood forecasting. This paper explores the application of water level, rainfall data and input time series into three different models; linear regression (LR), auto-regressive integrated moving average (ARIMA) and artificial neural networks (ANN). The performances of the models were compared based on the maximum coefficient of determination (R2) and minimum root means square error (RMSE). Based on the results the ANN model presents the most accurate measurement, with the R2 value of 0.868 and 18% RMSE. The present study suggests that ANN is the best model due to its ability to recognise times series patterns and to understand non-linear relationships. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group. Taylor & Francis 2019 Article PeerReviewed Khairuddin, Nuruljannah and Aris, Ahmad Zaharin and El-Shafie, Ahmed and Sheikhy Narany, Tahoora and Ishak, Mohd Yusoff and Isa, Noorain Mohd (2019) Efficient forecasting model technique for river stream flow in tropical environment. Urban Water Journal, 16 (3). pp. 183-192. ISSN 1573-062X, DOI https://doi.org/10.1080/1573062X.2019.1637906 <https://doi.org/10.1080/1573062X.2019.1637906>. https://doi.org/10.1080/1573062X.2019.1637906 doi:10.1080/1573062X.2019.1637906 |
| spellingShingle | TA Engineering (General). Civil engineering (General) Khairuddin, Nuruljannah Aris, Ahmad Zaharin El-Shafie, Ahmed Sheikhy Narany, Tahoora Ishak, Mohd Yusoff Isa, Noorain Mohd Efficient forecasting model technique for river stream flow in tropical environment |
| title | Efficient forecasting model technique for river stream flow in tropical environment |
| title_full | Efficient forecasting model technique for river stream flow in tropical environment |
| title_fullStr | Efficient forecasting model technique for river stream flow in tropical environment |
| title_full_unstemmed | Efficient forecasting model technique for river stream flow in tropical environment |
| title_short | Efficient forecasting model technique for river stream flow in tropical environment |
| title_sort | efficient forecasting model technique for river stream flow in tropical environment |
| topic | TA Engineering (General). Civil engineering (General) |
| work_keys_str_mv | AT khairuddinnuruljannah efficientforecastingmodeltechniqueforriverstreamflowintropicalenvironment AT arisahmadzaharin efficientforecastingmodeltechniqueforriverstreamflowintropicalenvironment AT elshafieahmed efficientforecastingmodeltechniqueforriverstreamflowintropicalenvironment AT sheikhynaranytahoora efficientforecastingmodeltechniqueforriverstreamflowintropicalenvironment AT ishakmohdyusoff efficientforecastingmodeltechniqueforriverstreamflowintropicalenvironment AT isanoorainmohd efficientforecastingmodeltechniqueforriverstreamflowintropicalenvironment |